Overview Stress tests in central banks & banking supervisory agencies Selected methodological issues Recent developments in stress test methodology
Stress Tests in FSAPs
Defining Stress Tests
Types of Stress Tests By aggregation Individual exposures Individual institutions System-wide - on bank by bank* data (“bottom up”)
- on aggregate data (“top down”)
Recent Experience with Stress Tests
Stress Tests in the FSAP
Stress Tests as a Multi-Stage Process
Stress Tests vs. Other Analytical Tools
Other Analytical Methods Complement Stress Tests
Stress Tests in Central Banks & Banking Supervisory Agencies
Role of Stress Tests Help focus supervisory processes on a risk basis Support macroprudential analysis at the central banks (including in financial stability reports) Assess effects of prospective policy changes
Stress Tests Conducted by Central Banks & Banking Supervisors Bank-by-bank stress tests conducted by supervisors Financial system stability reports include or refer to aggregate stress tests Range of approaches – examples: - Hungary (focus on sensitivity calculations)
- Norway (focus on sources of credit risk)
Implementation Issues
Coverage - Institutions
Coverage - Exposures
Methodology
Methodology
Organization How frequently? - Standard set of tests on quarterly basis, market risk/sensitivity analysis more frequently, elaborate analysis (e.g. contagion) less frequently
Run by whom? Which software? - Many start with Excel and E-Views, then integrate with supervisory information systems
- By bank (supervision; links with EWS)
- By peer groups (macroprudential surveillance)
Selected Methodological Issues
Selected Methodological Issues
Macro Scenarios for Stress Tests Historical scenarios - e.g. the 1997 turbulence and subsequent slowdown in East Asia
Hypothetical scenarios - Recognizing the limitations of macro models, especially for large shocks, would it be possible to use the central bank’s existing macro model?
- Stochastic simulations based on the model?
- Scenario design: relative sizes of shocks to the risk factors
- Assessing likelihood of the scenarios
Worst Case vs. Threshold Approach
Controversy on Probability
Controversy on Probability
Foreign Exchange Risk Note: C=capital, ARW=risk-weighted assets, F=net open position, e=exchange rate The impact on capital adequacy roughly equals the shock times the open position…
Foreign Exchange Risk ... but stress test must reflect non-linearity arising from FX options Off-balance sheet (OBS) positions, which include options, are not negligible in many countries. Example
Indirect FX Risk Nonperforming loans vs. exchange rate Usually much more significant than the direct FX risk The analysis requires - Regression of leverage vs. NPLs
- Inclusion of stock and flow exposures in FX
Interest Rate Risk Duration is the key indicator, because This allows to express changes in capital adequacy ratio as where
Interest Rate Risk—Issues Adequacy of the available data, including - Do banks report residual maturity properly?
- Does the indicator capture the whole balance sheet?
- Are off-balance sheet contracts included?
Simplified method: residual maturity plus weigths proposed by Basel Committee Nonlinearity (duration changes with large changes in interest rates) NPV may differ from the regulatory capital Correlation between risk-weighted assets and assets Indirect interest rate risk (see under credit risk)
Credit Risk Modeling The most significant source of risk Also, the most in need of strengthening - Mechanical approaches
- Approaches based on corporate sector data (leverage, interest coverage) & possibly household sector data
- Approaches based on loan performance data (including the VAR model already estimated)
1. Mechanical Approaches Assume an inflow of new NPLs - Function of existing NPLs, performing loans, or a weighted sum of the two
Assume ↑ provisions on existing NPLs Credit expansion model: inflow of new loans, followed by credit migration to and within NPLs Do the above by sectors (e.g. corporate & household)
2. Data on Borrowers Leverage vs. NPLs (a possible model) * Top-down calculations * Notes: Based on an actual model used by IMF staff for cross-country panel data estimates. Npls – ratio of non-performing loans to total loans, lev – leverage ratio, rcc – real cost of capital, reer – real effective exchange rate, y-hat – real GDP growth rate, p-hat – inflation rate, m-hat – growth rate of M1, d-hat – growth rate of domestic credit, roe – corporate sector return on equity
2. Data on Borrowers Logit model predicting individual bankruptcy probabilities as a function of age, size, industry characteristics & corporate soundness indicators (earnings, liquidity, financial strength) Include interest and exchange rates on the right hand side (to capture the indirect risk) Link to individual banks through their exposures to the various groups of companies Predict bank potential losses (also taking into account collateral)
2. Data on Borrowers A simpler approach: exposure variables - Net open FX position & ratio of FX income to FX costs (for indirect FX risk)
- Interest coverage (for indirect interest risk)
If exposure variable exceeds an estimated (assumed) threshold, default rate rises Similarly to previous approach, translate to bank losses (after collateral)
3. Loan Performance Data Advantages - Also available for household sector (with rapid lending growth in many countries)
- Should be more readily available than leverage
Disadvantage - Lagging indicators of asset quality
Introducing Contagion Risk Need to compile data for the following matrix
Introducing Contagion Risk Exposure = all uncollateralized lending (including both on- & off-balance sheet exposures) Currently, only data on total exposure of a bank to interbank market are available Two types of the contagion stress test - “Pure” contagion test: A “fraud” in a bank; impact on other banks through interbank exposures
- “Macro” contagion test: Macro shocks are grossed-up to trigger failure of weakest bank; followed by interbank contagion
Introducing Contagion Risk Implementation (example for 4 banks)
Introducing Contagion Risk Aggregate stress test vs. interbank contagion stress test
Equity & Real Estate Price Risk Equity price risk—similar to FX risk - Net open positions in equities
- Need to include off-balance sheet exposures
Banks’ exposure to real estate price risk - Direct exposure (investment in real estate)
- Credit exposure (developers etc.)
- Degree of real estate collateralization
- loan to value ratio
- default probability (from credit risk stress test)
Concentration Risks (Credit) Simple example: sensitivity analysis for large exposures - Run regressions for default probability on corporate data (company-by-company), with dummy variables for the sectors/regions
- Ways to define default probability (actual default—run a logit regression; or set a threshold for interest coverage ratio)
- For a set of a bank’s exposures to sectors/regions, calculate implied default probability
Liquidity Risk Focus on bank liquidity stress tests Results reported off-site, validate during on-site visits Off-site cross-check (sensitivity analysis) - Overall risk: assume a % of deposits withdrawn (percentages determined based on past bank runs, vary for different maturities)
- Concentration risk in deposits (same as above, but for a percentage of the largest deposits)
Recent Developments in Stress Test Methodology
Bank Internal Stress Test Models Two surveys of stress test practices in commercial banks (2000 & 2004) More attention to bank internal stress tests in on-site visits Consider issuing guidelines on stress tests in commercial banks? Cross-check results of bank & supervisor stress tests
Stress Tests vs. Early Warning Systems Consider designing an EWS system in the form of a statistical model of detection of bank failure/stress BIS working paper on EWS for banking supervision
Cross-Market Contagion Coverage of non-bank financial institutions in the framework for consolidated supervision Contagion between banks and non-bank financial institutions—e.g. insurance companies Credit derivatives
Further Reading Blaschke et al., 2001, “ST of Financial Systems: An Overview of Issues, Methodologies, and FSAP Experiences,” IMF WP 01/88 - www.imf.org/external/pubs/cat/longres.cfm?sk=15166.0
IMF & WB, 2003, “Analytical Tools of the FSAP” - www.imf.org/external/np/fsap/2003/022403a.pdf
Čihák, 2004, “ST: A Review of Key Concepts,” Czech National Bank technical note 2/2004 - http://www.cnb.cz/en/pdf/IRPN_2_2004.pdf
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